TY - JOUR
T1 - Domain Adaptation of Binary Sensors in Smart Environments Through Activity Alignment
AU - Polo-Rodriguez, Aurora
AU - Cruciani, Federico
AU - Nugent, CD
AU - Medina, Javier
PY - 2020/12/21
Y1 - 2020/12/21
N2 - Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.
AB - Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.
KW - Sensor Translation
KW - Domain Adaptation
KW - Smart Environments
U2 - 10.1109/ACCESS.2020.3046181
DO - 10.1109/ACCESS.2020.3046181
M3 - Article
VL - 8
SP - 228804
EP - 228817
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -